{
 "cells": [
  {
   "cell_type": "raw",
   "id": "fd941e69-fb82-4ed5-b581-2ddd6d652399",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x=np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])\n",
    "y=np.array([[2000],[2200],[4900],[3221],[6834],[10567],[9566],[15678],[13644],[15789]])\n",
    "model=LinearRegression()\n",
    "model.fit(x,y)\n",
    "print(\"w=\",model.coef_[0],\"b=\",model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "237a2687-9601-4431-a2f7-90e06c6906f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w= [1702.22424242] b= [-922.33333333]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x=np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])\n",
    "y=np.array([[2000],[2200],[4900],[3221],[6834],[10567],[9566],[15678],[13644],[15789]])\n",
    "model=LinearRegression()\n",
    "model.fit(x,y)\n",
    "print(\"w=\",model.coef_[0],\"b=\",model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7bacfd61-ddc5-4422-8dc9-3745b777c928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "y2=model.predict(x)\n",
    "plt.xlabel('面积')\n",
    "plt.ylabel('售价')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([0,11,1900,20000])\n",
    "plt.scatter(x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,y2,'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a71d744a-f583-4fe6-833b-5063b6c61906",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=model.predict([["
   ]
  }
 ],
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